


import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    StableDiffusionSAGPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()


class     __UpperCAmelCase							(       __A ,					__A ,					unittest.TestCase							):


     """simple docstring"""
     _lowerCamelCase   =      StableDiffusionSAGPipeline
     _lowerCamelCase   =      TEXT_TO_IMAGE_PARAMS
     _lowerCamelCase   =      TEXT_TO_IMAGE_BATCH_PARAMS
     _lowerCamelCase   =      TEXT_TO_IMAGE_IMAGE_PARAMS
     _lowerCamelCase   =      TEXT_TO_IMAGE_IMAGE_PARAMS
     _lowerCamelCase   =      False


     def 	snake_case_  (	self    ):
        torch.manual_seed(0    )
        __a					=  UNetaDConditionModel(
            block_out_channels=(32, 64)  ,       layers_per_block=2  ,       sample_size=32  ,       in_channels=4  ,       out_channels=4  ,       down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")  ,       up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""")  ,       cross_attention_dim=32  ,       )
        __a					=  DDIMScheduler(
            beta_start=0.00085  ,       beta_end=0.012  ,       beta_schedule="""scaled_linear"""  ,       clip_sample=__A  ,       set_alpha_to_one=__A  ,       )
        torch.manual_seed(0    )
        __a					=  AutoencoderKL(
            block_out_channels=[32, 64]  ,       in_channels=3  ,       out_channels=3  ,       down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""]  ,       up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""]  ,       latent_channels=4  ,       )
        torch.manual_seed(0    )
        __a					=  CLIPTextConfig(
            bos_token_id=0  ,       eos_token_id=2  ,       hidden_size=32  ,       intermediate_size=37  ,       layer_norm_eps=1E-05  ,       num_attention_heads=4  ,       num_hidden_layers=5  ,       pad_token_id=1  ,       vocab_size=1000  ,       )
        __a					=  CLIPTextModel(__A    )
        __a					=  CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""    )

        __a					=  {
            """unet""": unet,
            """scheduler""": scheduler,
            """vae""": vae,
            """text_encoder""": text_encoder,
            """tokenizer""": tokenizer,
            """safety_checker""": None,
            """feature_extractor""": None,
        }
        return components


     def 	snake_case_  (	self  ,       __A  ,       __A=0    ):
        if str(__A    ).startswith("""mps"""    ):
           __a					=  torch.manual_seed(__A    )
        else:
           __a					=  torch.Generator(device=__A    ).manual_seed(__A    )
        __a					=  {
            """prompt""": """.""",
            """generator""": generator,
            """num_inference_steps""": 2,
            """guidance_scale""": 1.0,
            """sag_scale""": 1.0,
            """output_type""": """numpy""",
        }
        return inputs


     def 	snake_case_  (	self    ):
        super().test_inference_batch_single_identical(expected_max_diff=3E-3    )







@slow
@require_torch_gpu
class     __UpperCAmelCase							(       unittest.TestCase							):


     """simple docstring"""


     def 	snake_case_  (	self    ):
        # clean up the VRAM after each test
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()


     def 	snake_case_  (	self    ):
        __a					=  StableDiffusionSAGPipeline.from_pretrained("""CompVis/stable-diffusion-v1-4"""    )
        __a					=  sag_pipe.to(__A    )
        sag_pipe.set_progress_bar_config(disable=__A    )

        __a					=  """."""
        __a					=  torch.manual_seed(0    )
        __a					=  sag_pipe(
            [prompt]  ,       generator=__A  ,       guidance_scale=7.5  ,       sag_scale=1.0  ,       num_inference_steps=20  ,       output_type="""np"""    )

        __a					=  output.images

        __a					=  image[0, -3:, -3:, -1]

        assert image.shape == (1, 512, 512, 3)
        __a					=  np.array([0.1568, 0.1738, 0.1695, 0.1693, 0.1507, 0.1705, 0.1547, 0.1751, 0.1949]    )

        assert np.abs(image_slice.flatten() - expected_slice    ).max() < 5E-2


     def 	snake_case_  (	self    ):
        __a					=  StableDiffusionSAGPipeline.from_pretrained("""stabilityai/stable-diffusion-2-1-base"""    )
        __a					=  sag_pipe.to(__A    )
        sag_pipe.set_progress_bar_config(disable=__A    )

        __a					=  """."""
        __a					=  torch.manual_seed(0    )
        __a					=  sag_pipe(
            [prompt]  ,       generator=__A  ,       guidance_scale=7.5  ,       sag_scale=1.0  ,       num_inference_steps=20  ,       output_type="""np"""    )

        __a					=  output.images

        __a					=  image[0, -3:, -3:, -1]

        assert image.shape == (1, 512, 512, 3)
        __a					=  np.array([0.3459, 0.2876, 0.2537, 0.3002, 0.2671, 0.2160, 0.3026, 0.2262, 0.2371]    )

        assert np.abs(image_slice.flatten() - expected_slice    ).max() < 5E-2


     def 	snake_case_  (	self    ):
        __a					=  StableDiffusionSAGPipeline.from_pretrained("""stabilityai/stable-diffusion-2-1-base"""    )
        __a					=  sag_pipe.to(__A    )
        sag_pipe.set_progress_bar_config(disable=__A    )

        __a					=  """."""
        __a					=  torch.manual_seed(0    )
        __a					=  sag_pipe(
            [prompt]  ,       width=768  ,       height=512  ,       generator=__A  ,       guidance_scale=7.5  ,       sag_scale=1.0  ,       num_inference_steps=20  ,       output_type="""np"""  ,       )

        __a					=  output.images

        assert image.shape == (1, 512, 768, 3)









'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available


lowerCAmelCase_     						=				{}

try:
    if not is_sentencepiece_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCAmelCase_     						=				['''GPTSw3Tokenizer''']


if TYPE_CHECKING:
    try:
        if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_gpt_swa import GPTSwaTokenizer

else:
    import sys

    lowerCAmelCase_     						=				_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


